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ORCIDMatthew Mirman
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2020 – today
- 2024
[j2]Gabriel Reuben Smith
, Carolina Bello, Lalasia Bialic-Murphy, Emily Clark, Camille S. Delavaux, Camille Fournier de Lauriere, Johan van den Hoogen, Thomas Lauber, Haozhi Ma, Daniel S. Maynard, Matthew Mirman, Lidong Mo, Dominic Rebindaine, Josephine Elena Reek, Leland K. Werden, Zhaofei Wu, Gayoung Yang, Qingzhou Zhao, Constantin M. Zohner, Thomas Ward Crowther:
Ten simple rules for using large language models in science, version 1.0. PLoS Comput. Biol. 20(1) (2024)- 2023
- [i6]Abhigya Sodani, Lauren Moos, Matthew Mirman:
LLM Guided Inductive Inference for Solving Compositional Problems. CoRR abs/2309.11688 (2023) - 2022
- [b1]Matthew Mirman:
Certified Deep Learning: Verification and Training. ETH Zurich, Zürich, Switzerland, 2022 - [j1]Matthew Mirman, Maximilian Baader, Martin T. Vechev:
The Fundamental Limits of Neural Networks for Interval Certified Robustness. Trans. Mach. Learn. Res. 2022 (2022) - 2021
- [c6]Matthew Mirman, Alexander Hägele, Pavol Bielik, Timon Gehr, Martin T. Vechev:
Robustness certification with generative models. PLDI 2021: 1141-1154 - [i5]Matthew Mirman, Maximilian Baader, Martin T. Vechev:
The Fundamental Limits of Interval Arithmetic for Neural Networks. CoRR abs/2112.05235 (2021) - 2020
- [c5]Maximilian Baader, Matthew Mirman, Martin T. Vechev:
Universal Approximation with Certified Networks. ICLR 2020 - [i4]Matthew Mirman, Timon Gehr, Martin T. Vechev:
Robustness Certification of Generative Models. CoRR abs/2004.14756 (2020)
2010 – 2019
- 2019
- [i3]Matthew Mirman, Gagandeep Singh, Martin T. Vechev:
A Provable Defense for Deep Residual Networks. CoRR abs/1903.12519 (2019) - [i2]Maximilian Baader, Matthew Mirman, Martin T. Vechev:
Universal Approximation with Certified Networks. CoRR abs/1909.13846 (2019) - [i1]Marc Fischer, Matthew Mirman, Steven Stalder, Martin T. Vechev:
Online Robustness Training for Deep Reinforcement Learning. CoRR abs/1911.00887 (2019) - 2018
- [c4]Matthew Mirman, Dimitar K. Dimitrov, Pavle Djordjevic, Timon Gehr, Martin T. Vechev:
Training Neural Machines with Trace-Based Supervision. ICML 2018: 3566-3574 - [c3]Matthew Mirman, Timon Gehr, Martin T. Vechev:
Differentiable Abstract Interpretation for Provably Robust Neural Networks. ICML 2018: 3575-3583 - [c2]Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev:
Fast and Effective Robustness Certification. NeurIPS 2018: 10825-10836 - [c1]Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin T. Vechev:
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation. IEEE Symposium on Security and Privacy 2018: 3-18
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